---
title: "Example: Graph RAG | RAG"
description: Example of implementing a Graph RAG system in Mastra using OpenAI embeddings and PGVector for vector storage.
---

import GithubLink from "@site/src/components/GithubLink";

# Graph RAG

This example demonstrates how to implement a Retrieval-Augmented Generation (RAG) system using Mastra, OpenAI embeddings, and PGVector for vector storage.

## Overview

The system implements Graph RAG using Mastra and OpenAI. Here's what it does:

1. Sets up a Mastra agent with gpt-5.1 for response generation
2. Creates a GraphRAG tool to manage vector store interactions and knowledge graph creation/traversal
3. Chunks text documents into smaller segments
4. Creates embeddings for these chunks
5. Stores them in a PostgreSQL vector database
6. Creates a knowledge graph of relevant chunks based on queries using GraphRAG tool
   - Tool returns results from vector store and creates knowledge graph
   - Traverses knowledge graph using query
7. Generates context-aware responses using the Mastra agent

## Setup

### Environment Setup

Make sure to set up your environment variables:

```bash title=".env"
OPENAI_API_KEY=your_openai_api_key_here
POSTGRES_CONNECTION_STRING=your_connection_string_here
```

### Dependencies

Then, import the necessary dependencies:

```typescript copy showLineNumbers title="index.ts"
import { Mastra } from "@mastra/core";
import { Agent } from "@mastra/core/agent";
import { ModelRouterEmbeddingModel } from "@mastra/core/llm";
import { PgVector } from "@mastra/pg";
import { MDocument, createGraphRAGTool } from "@mastra/rag";
import { embedMany } from "ai";
```

## GraphRAG Tool Creation

Using createGraphRAGTool imported from @mastra/rag, you can create a tool that queries the vector database and converts the results into a knowledge graph:

```typescript copy showLineNumbers{8} title="index.ts"
const graphRagTool = createGraphRAGTool({
  vectorStoreName: "pgVector",
  indexName: "embeddings",
  model: new ModelRouterEmbeddingModel("openai/text-embedding-3-small"),
  graphOptions: {
    dimension: 1536,
    threshold: 0.7,
  },
});
```

## Agent Configuration

Set up the Mastra agent that will handle the responses:

```typescript copy showLineNumbers{19} title="index.ts"
const ragAgent = new Agent({
  id: "rag-agent",
  name: "GraphRAG Agent",
  instructions: `You are a helpful assistant that answers questions based on the provided context. Format your answers as follows:

1. DIRECT FACTS: List only the directly stated facts from the text relevant to the question (2-3 bullet points)
2. CONNECTIONS MADE: List the relationships you found between different parts of the text (2-3 bullet points)
3. CONCLUSION: One sentence summary that ties everything together

Keep each section brief and focus on the most important points.

Important: When asked to answer a question, please base your answer only on the context provided in the tool.
If the context doesn't contain enough information to fully answer the question, please state that explicitly.`,
  model: "openai/gpt-5.1",
  tools: {
    graphRagTool,
  },
});
```

## Instantiate PgVector and Mastra

Instantiate PgVector and Mastra with the components:

```typescript copy showLineNumbers{36} title="index.ts"
const pgVector = new PgVector({
  id: 'rag-vectors',
  connectionString: process.env.POSTGRES_CONNECTION_STRING!,
});

export const mastra = new Mastra({
  agents: { ragAgent },
  vectors: { pgVector },
});
const agent = mastra.getAgent("ragAgent");
```

## Document Processing

Create a document and process it into chunks:

```typescript copy showLineNumbers{45} title="index.ts"
const doc = MDocument.fromText(`
# Riverdale Heights: Community Development Study
// ... text content ...
`);

const chunks = await doc.chunk({
  strategy: "recursive",
  size: 512,
  overlap: 50,
  separator: "\n",
});
```

## Creating and Storing Embeddings

Generate embeddings for the chunks and store them in the vector database:

```typescript copy showLineNumbers{56} title="index.ts"
const { embeddings } = await embedMany({
  model: new ModelRouterEmbeddingModel("openai/text-embedding-3-small"),
  values: chunks.map((chunk) => chunk.text),
});

const vectorStore = mastra.getVector("pgVector");
await vectorStore.createIndex({
  indexName: "embeddings",
  dimension: 1536,
});
await vectorStore.upsert({
  indexName: "embeddings",
  vectors: embeddings,
  metadata: chunks?.map((chunk: any) => ({ text: chunk.text })),
});
```

## Graph-Based Querying

Try different queries to explore relationships in the data:

```typescript copy showLineNumbers{82} title="index.ts"
const queryOne =
  "What are the direct and indirect effects of early railway decisions on Riverdale Heights' current state?";
const answerOne = await ragAgent.generate(queryOne);
console.log("\nQuery:", queryOne);
console.log("Response:", answerOne.text);

const queryTwo =
  "How have changes in transportation infrastructure affected different generations of local businesses and community spaces?";
const answerTwo = await ragAgent.generate(queryTwo);
console.log("\nQuery:", queryTwo);
console.log("Response:", answerTwo.text);

const queryThree =
  "Compare how the Rossi family business and Thompson Steel Works responded to major infrastructure changes, and how their responses affected the community.";
const answerThree = await ragAgent.generate(queryThree);
console.log("\nQuery:", queryThree);
console.log("Response:", answerThree.text);

const queryFour =
  "Trace how the transformation of the Thompson Steel Works site has influenced surrounding businesses and cultural spaces from 1932 to present.";
const answerFour = await ragAgent.generate(queryFour);
console.log("\nQuery:", queryFour);
console.log("Response:", answerFour.text);
```

<br />
<br />
<hr className="dark:border-[#404040] border-gray-300" />
<br />
<br />
<GithubLink
  href={
    "https://github.com/mastra-ai/mastra/blob/main/examples/basics/rag/graph-rag"
  }
/>
